Mapping fractures with GPR: A case study from Turtle Mountain
Bibliographic record
Abstract
Abstract Ground-penetrating radar (GPR) surveys were acquired of rocks on the highly fractured summit of Turtle Mountain in Canada. In 1903 a disastrous rock slide occurred at Turtle Mountain and it still poses a geologic hazard. Dips, shapes, and penetration depths of fractures are important parameters in slope-stability analysis. Determination of fracture orientation at Turtle Mountain has been based mostly on areal geologic mapping and, most recently, on data collected from boreholes. The purpose of GPR surveys was to test, confirm, and extend information about fractures and bedding planes. Data acquisition was complicated by the rough terrain; because slopes are steep and uneven. This also complicated analysis of the data. Measurement of in situ velocity — an important value for migration — was impossible. Instead, data were migrated with different velocities and data results were chosen that were considered to be reasonable. Analysis and interpretation of the data, resulted in confirmation and extension of the a priori information on orientations of fractures and bedding planes at Turtle Mountain. Despite the rough terrain and highly fractured rock mass, GPR surveys provide reliable information about the shapes and density of fractures — information important for slope-stability evaluation. The most reliable migration results obtained for velocities were considerably less than the standard velocities recorded for limestone, the dominant lithofacies at Turtle Mountain. We interpret this observation as an indicator of water within the rock. However, thorough investigation of this conclusion remains a project for future work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".